DBMS > Apache Hive vs. Apache Phoenix vs. Dolt

System Properties Comparison Apache Hive vs. Apache Phoenix vs. Dolt

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Editorial information provided by DB-Engines

Name

Description

data warehouse software for querying and managing large distributed datasets, built on Hadoop

A scale-out RDBMS with evolutionary schema built on Apache HBase

A MySQL compatible DBMS with Git-like versioning of data and schema

Primary database model

Relational DBMS

Secondary database models

Document store

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	75.77
Rank	#16	Overall
	#11	Relational DBMS

Score	1.82
Rank	#124	Overall
	#60	Relational DBMS

Score	0.60
Rank	#232	Overall
	#106	Relational DBMS

Website

hive.apache.org

phoenix.apache.org

github.com/dolthub/dolt
www.dolthub.com

Technical documentation

cwiki.apache.org/confluence/display/Hive/Home

phoenix.apache.org

docs.dolthub.com

Developer

Apache Software Foundation

initially developed by Facebook

Apache Software Foundation

DoltHub Inc

Initial release

2012

2014

2018

Current release

3.1.3, April 2022

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Server operating systems

All OS with a Java VM

Linux
Unix
Windows

Linux
macOS
Windows

Data scheme

yes

late-bound, schema-on-read capabilities

yes

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC
Thrift

JDBC

CLI Client
HTTP REST

Supported programming languages

C++
Java
PHP
Python

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

yes

currently in alpha release

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency or Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Access rights for users, groups and roles

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

Only one user is configurable, and must be specified in the config file at startup

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More resources

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